Chat with us, powered by LiveChat A marketing company based out of New York City is doing well and is looking to expand internationally. The CEO and VP of Operations decide to enlist the help of a consulting fi - Essayabode

A marketing company based out of New York City is doing well and is looking to expand internationally. The CEO and VP of Operations decide to enlist the help of a consulting fi

 Instructions

Final Project Assignment Instructions

Scenario Background:

A marketing company based out of New York City is doing well and is looking to expand internationally. The CEO and VP of Operations decide to enlist the help of a consulting firm that you work for, to help collect data and analyze market trends.

You work for Mercer Human Resources. The Mercer Human Resource Consulting website lists prices of certain items in selected cities around the world. They also report an overall cost-of-living index for each city compared to the costs of hundreds of items in New York City (NYC). For example, London at 88.33 is 11.67% less expensive than NYC.

More specifically, if you choose to explore the website further you will find a lot of fun and interesting data. You can explore the website more on your own after the course concludes.

https://mobilityexchange.mercer.com/Insights/ cost-of-living-rankings#rankings

Assignment Guidance:

In the Excel document, you will find the 2018 data for 17 cities in the data set Cost of Living. Included are the 2018 cost of living index, cost of a 3-bedroom apartment (per month), price of monthly transportation pass, price of a mid-range bottle of wine, price of a loaf of bread (1 lb.), the price of a gallon of milk and price for a 12 oz. cup of black coffee. All prices are in U.S. dollars.

You use this information to run a Multiple Linear Regression to predict Cost of living, along with calculating various descriptive statistics. This is given in the Excel output (that is, the MLR has already been calculated. Your task is to interpret the data).

Based on this information, in which city should you open a second office in? You must justify your answer. If you want to recommend 2 or 3 different cities and rank them based on the data and your findings, this is fine as well.

Deliverable Requirements:

This should be ¾ to 1 page, no more than 1 single-spaced page in length, using 12-point Times New Roman font. You do not need to do any calculations, but you do need to pick a city to open a second location at and justify your answer based upon the provided results of the Multiple Linear Regression.

The format of this assignment will be an Executive Summary. Think of this assignment as the first page of a much longer report, known as an Executive Summary, that essentially summarizes your findings briefly and at a high level. This needs to be written up neatly and professionally. This would be something you would present at a board meeting in a corporate environment. If you are unsure of an Executive Summary, this resource can help with an overview. How to Write an Executive Summary That Gets the Job Done 2023.pdf

Things to Consider:

To help you make this decision here are some things to consider:

  • Based on the MLR output, what variable(s) is/are significant?
  • From the significant predictors, review the mean, median, min, max, Q1 and Q3 values?
    • It might be a good idea to compare these values to what the New York value is for that variable. Remember New York is the baseline as that is where headquarters are located.
  • Based on the descriptive statistics, for the significant predictors, what city has the best potential?
    • What city or cities fall are below the median?
    • What city or cities are in the upper 3rd quartile?

Final MLR

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.9358240783
R Square 0.8757667056
Adjusted R Square 80.12%
Standard Error 8.3094532099
Observations 17
ANOVA
df SS MS F Significance F
Regression 6 4867.380767635 811.2301279392 11.748953312 0.0004996299
Residual 10 690.4701264826 69.0470126483
Total 16 5557.8508941176
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 35.6395017829 15.4187693276 2.3114362129 0.0434011406 1.2843427942 69.9946607717 1.2843427942 69.9946607717
Rent (in City Centre) -0.0032128517 0.003974813 -0.8083026028 0.4377227847 -0.0120692871 0.0056435836 -0.0120692871 0.0056435836
Monthly Pubic Trans Pass 0.2996500033 0.0769640509 3.8933761896 0.0029930715 0.1281634113 0.4711365954 0.1281634113 0.4711365954
Loaf of Bread 16.5948178712 6.7133012492 2.4719310597 0.0329955879 1.6366505328 31.5529852097 1.6366505328 31.5529852097
Milk 2.9120817057 1.9894114601 1.4637905552 0.1739643111 -1.5206032612 7.3447666725 -1.5206032612 7.3447666725
Bottle of Wine (mid-range) -0.8898054861 0.7401902965 -1.2021307093 0.2570060814 -2.5390522435 0.7594412713 -2.5390522435 0.7594412713
Coffee -2.5274380534 6.4845553577 -0.3897627384 0.7048842587 -16.9759277837 11.9210516769 -16.9759277837 11.9210516769
RESIDUAL OUTPUT
Observation Predicted Cost of Living Index Residuals Standard Residuals City
1 34.3260713681 -2.5860713681 -0.3936661298 Mumbai
2 53.2165605253 -2.2665605253 -0.3450284168 Prague
3 49.4143612149 -3.9643612149 -0.6034770563 Warsaw
4 58.6361178497 4.4238821503 0.6734278823 Athens
5 73.0844953758 5.1055046242 0.7771882365 Rome
6 86.5025600265 -3.0525600265 -0.4646776212 Seoul
7 75.8921691573 6.3078308427 0.9602130034 Brussels
8 67.7257781049 -0.9757781049 -0.1485383562 Madrid
9 90.5199607051 -16.4599607051 -2.5056265297 Vancouver
10 81.0735873148 8.8664126852 1.3496945251 Paris
11 83.8056463253 9.1343536747 1.3904819889 Tokyo
12 80.02510391 -8.37510391 -1.2749047778 Berlin
13 82.4162431846 3.4837568154 0.5303167885 Amsterdam
14 97.7565481074 2.2434518926 0.3415106926 New York
15 87.7399392431 3.0400607569 0.4627749131 Sydney
16 86.8166829103 1.1133170897 0.1694753035 Dublin
17 94.3681746768 -6.0381746768 -0.9191644459 London

Data

City Cost of Living Index Rent (in City Centre) Monthly Pubic Trans Pass Loaf of Bread Milk Bottle of Wine (mid-range) Coffee
Mumbai 31.74 $1,642.68 $7.66 $0.41 $2.93 $10.73 $1.63
Prague 50.95 $1,240.48 $25.01 $0.92 $3.14 $5.46 $2.17
Warsaw 45.45 $1,060.06 $30.09 $0.69 $2.68 $6.84 $1.98
Athens 63.06 $569.12 $35.31 $0.80 $5.35 $8.24 $2.88
Rome 78.19 $2,354.10 $41.20 $1.38 $6.82 $7.06 $1.51
Seoul 83.45 $2,370.81 $50.53 $2.44 $7.90 $17.57 $1.79
Brussels 82.2 $1,734.75 $57.68 $1.66 $4.17 $8.24 $1.51
Madrid 66.75 $1,795.10 $64.27 $1.04 $3.63 $5.89 $1.58
Vancouver 74.06 $2,937.27 $74.28 $2.28 $7.12 $14.38 $1.47
Paris 89.94 $2,701.61 $85.92 $1.56 $4.68 $8.24 $1.51
Tokyo 92.94 $2,197.03 $88.77 $1.77 $6.46 $17.75 $1.49
Berlin 71.65 $1,695.77 $95.34 $1.24 $3.52 $5.89 $1.71
Amsterdam 85.9 $2,823.28 $105.93 $1.33 $4.34 $7.06 $1.71
New York 100 $5,877.45 $121.00 $2.93 $3.98 $15.00 $0.84
Sydney 90.78 $3,777.72 $124.55 $1.94 $4.43 $14.01 $2.26
Dublin 87.93 $3,025.83 $144.78 $1.37 $4.31 $14.12 $2.06
London 88.33 $4,069.99 $173.81 $1.23 $4.63 $10.53 $1.90
mean 75.49 $2,463.12 $78.01 $1.47 $4.71 $10.41 $1.76
median 82.2 $2,354.10 $74.28 $1.37 $4.34 $8.24 $1.71
min 31.74 $569.12 $7.66 $0.41 $2.68 $5.46 $0.84
max 100 $5,877.45 $173.81 $2.93 $7.90 $17.75 $2.88
Q1 66.75 $1,695.77 $41.20 $1.04 $3.63 $7.06 $1.51
Q3 88.33 $2,937.27 $105.93 $1.77 $5.35 $14.12 $1.98
New York 100 $5,877.45 $121.00 $2.93 $3.98 $15.00 $0.84

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